***** OPEN OUTPUT LOG FILE FOR MANUSCRIPT ANALYSES: "HARDIWRING MUTUAL COMMITTMENT" *****


*log "C:\Users\gk57526\Dropbox\Confirmation Dynamics Project (Jason Byers)\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Output\Hardwiring Committment.MANUSCRIPT.04-21-2023.smcl" 

log using "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\Dart (PRQ)\Output\Hardwiring Committment.MANUSCRIPT.04-21-2023.smcl", replace






**** MANUSCRIPT STATISTICAL ANALYSES: INCLUDING STATISTICS NOTED IN THE TEXT ***







** RETRIEVE SINGLE EVENT RECORDS DATABASE [N = 860 APPOINTEE OBSERVATIONS: 831 UNCENSORED; 29 CENSORED] **
*use "C:\Users\gk57526\Dropbox\Confirmation Dynamics Project (Jason Byers)\Appointee Tenure Project\Jason Byers\March 2023\Data\Krause and Byers.SRD.06-03-2022.dta" 

*
use "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Data\Krause and Byers.SRD.06-03-2022.dta", replace

*
*





** GENERATE CENSORING VARIABLE FOR HOLDOVER APPOINTEES SERVING BETWEEN/ACROSS ADMINISTRATIONS [=1]; UNCENSRED OBSERVATIONS [=0] ** 

gen singleadmin_service=1 if holdover==0
*
replace singleadmin_service=0 if holdover==1
*
*
tab singleadmin_service


** SET FOR SURVIVAL DATA WITH A SINGLE RECORD PER APPOINTEE OBSERVATION [N = 860: UNCENSORED N = 831; CENSORED N = 29] ** 
stset okapptdur, failure(singleadmin_service)
*
*
*
*

** ESTIMATE COX SEMIPARAMETRIC AND WEIBULL PARAMETRIC MODELS PRESENTED IN MANUSCRIPT [TABLE 1: MODELS 1 - 4] ** 

** NOTE COVARIATES THAT VARY TRHOUGH TIME ARE BASED ON THE STARTING DATE OF APPOINTED SERVICE [I.E., "OKSTART....""]




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*** COMPUTE TAB ON FULL SAMPLE OF N = 860 TO EVALUATE COMMONALITY OF SHORT TENURES ***

tab okapptdur

*
*
*

*** COMPUTE TAB ON TRUNCATED SAMPLE EXCLUDING LAST TWO YEARS OF EACH PRESIDENTIAL TERM: N = 610 [70.93% OF FULL SAMPLE] TO EVALUATE COMMONALITY OF SHORT TENURES ***

tab okapptdur if okstartadyr==1 | okstartadyr==2 | okstartadyr==5 | okstartadyr==6

*
*
*
*

**** SHOW DIFFERENCES BETWEEN HIGH LOYALISTS WITH A MUTUAL POLICY COMMITTMENT [highloyalpp==1, n=236] AND HIGH LOYALISTS THAT DO NOT [highloyalpp==0, n=100]: where HIGH LOYALISTS ARE DEFINED AS "zloyalmedian>=0" [LIES BETWEEN MEDIAN = -0.1646416 & MEAN = 0.1284699: 61st Percentile] ***

sum zloyalmedian, detail



*
gen highloyalpp = 1 if zloyalmedian>=0 & soubinaryagency2nom==1
*
replace highloyalpp = 0 if zloyalmedian>=0 & soubinaryagency2nom==0



*** NO SUBSTANTIVE DIFFERENCES INVOLVING PRESIDENTIAL LOYALTY BETWEEN THE TWO TYPES [HIGH LOYALTY-POLICY PRIORITY VERSUS HIGH LOYALTY-NON POLICY PRIORITY] ***

ttest zloyalmedian, by(highloyalpp) reverse unequal welch




*** HIGHER POLICY AND MANAGERIAL COMPETENCE FOR HIGH LOYALTY-POLICY PRIORITY TYPES COMPARED TO HIGH LOYALTY-NON POLICY PRIORITY TYPES ***

ttest zpecompmedian, by(highloyalpp) reverse unequal welch


ttest zmecompmedian, by(highloyalpp) reverse unequal welch



*** APPOINTEE TENURE IS NOT HIGHER FOR PRIORTY EXECUTIVE AGENCIES COMPARED TO NON-PRIORITY EXECUTIVE AGENCIES - ACTUALLY THE OPPOSITE IS TRUE IN AN UNCONDITIONAL SENSE ***

ttest okapptdur, by(soubinaryagency2nom) reverse unequal



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*** COMPUTE TITLE: FIGURE 1A: HISTOGRAM AND KERNEL DENSITY PLOT OF APPOINTEE TENURE (SUBTITLE: U.S. EXECUTIVE AGENCY LEADERSHIP POSITIONS) **** 

*
kdensity okapptdur, lcolor(black) addplot((histogram okapptdur, fcolor(gs10)), below) legend(off) ylabel(0(0.0002)0.0008, angle(0) labsize(small)) xlabel(0(1000)4000, angle(0) labsize(small)) note("") ytitle("Density", size(small) margin(r=2.5)) xtitle("Total Appointee Duration (Days)", size(small) margin(t=2)) title("FIGURE 1A: Histogram & Kernel Density Plot of Administrative Leaders' Tenure Duration", size(small)) subtitle("(U.S. Federal Executive Agencies)", size(small))
*
graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\Figure1a.gph", replace




*** COMPUTE TITLE: FIGURE 1B: KAPLAN-MEIER SURVIVOR ESTIMATES OF APPOINTEE TENURE (SUBTITLE: U.S. EXECUTIVE AGENCY LEADERSHIP POSITIONS) **** 


**** JASON, PLEASE MAKE SURE THAT THE UNIVARIATE STATISTICS BELOW ARE BASED ON THE FULL SET OF 860 APPOINTEE OBSERRVATIONS ***

sts graph, ylabel(0(0.25)1, angle(0) labsize(small) nogrid) xline(907, lcolor(red%60) lpattern(dash)) xlabel(0(1000)4000, angle(0) labsize(small)) note("") ytitle("Survival", size(small) margin(r=2.5)) xtitle("Total Appointee Duration (Days)", size(small) margin(t=2)) title("FIGURE 1B: Kaplan-Meier Survivor Estimates of Administrative Leaders' Tenure Duration", size(small)) subtitle("(U.S. Federal Executive Agencies)", size(small))

graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\Figure1b.gph", replace







*******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************




*** MANUSCRIPT SURVIVAL REGRESSION ANALYSES: COX SEMIPARAMETRIC & WEIBULL PARAMETRIC MODELS ****




******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************




**** MANUSCRIPT REGRESSION MODELS  ***



**** MODEL 1: COX MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****

stcox   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i.okstartadyr  ,  hr vce(cluster sbagency)
*
estat ic
*
* Assess the Total Effect of Appointee Loyalty on Appointee Tenure in Priority Agencies: Priority Agency as Conditioning Covariate * 
lincom  zloyalmedian + 1.soubinaryagency2nom#c.zloyalmedian, hr
*
* Assess the Total Effect of Appointee Loyalty on Appointee Tenure in Priority Agencies: Appointee Loyalty as Conditioning Covariate * 
lincom  1.soubinaryagency2nom + 1.soubinaryagency2nom#c.zloyalmedian, hr
*
*
*
estimates store model1
estout model1, cells(b(star fmt(3)) se(par fmt(3))) eform


*** COMPUTE Figure 2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {PP − NPP Difference} {{4 [M1−M4] × 1 Horizontal Point Estimates and 95% CIs}}. ****
** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]



** ONE INTERQUARTILE RANGE MARGINAL EFFECT INCREASE IN APPOINTEE LOYALTY DIFFERENTIAL BETWEEN POLICY PRIORITY AGENCY VERSUS NON-POLICY PRIORITY AGENCY [FIGURE 2] **

lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3653231, eform(hr)
matrix model1zloyal = r(table)
mat list model1zloyal
*







******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************



**** MODEL 2: COX MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****

stcox   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i.okstartadyr  i.sbagency reagan bush41 clinton bush43,  hr vce(cluster sbagency)
*
estat ic
*
* Assess the Total Effect of Appointee Loyalty on Appointee Tenure in Priority Agencies: Priority Agency as Conditioning Covariate * 
lincom  zloyalmedian + 1.soubinaryagency2nom#c.zloyalmedian, hr
*
* Assess the Total Effect of Appointee Loyalty on Appointee Tenure in Priority Agencies: Appointee Loyalty as Conditioning Covariate * 
lincom  1.soubinaryagency2nom + 1.soubinaryagency2nom#c.zloyalmedian, hr
*
*
*
estimates store model2
estout model2, cells(b(star fmt(3)) se(par fmt(3))) eform


*** COMPUTE Figure 2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {PP − NPP Difference} {{4 [M1−M4] × 1 Horizontal Point Estimates and 95% CIs}}. ****
** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]

lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3653231, eform(hr)
matrix model2zloyal = r(table)
mat list model2zloyal





*******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************




**** MODEL 3: WEIBULL MODEL [OMISSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****

streg   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr,   distribution(weibull)  hr vce(cluster sbagency)
*
estat ic
*
* Assess the Total Effect of Appointee Loyalty on Appointee Tenure in Priority Agencies: Priority Agency as Conditioning Covariate * 
lincom  zloyalmedian + 1.soubinaryagency2nom#c.zloyalmedian, hr
*
* Assess the Total Effect of Appointee Loyalty on Appointee Tenure in Priority Agencies: Appointee Loyalty as Conditioning Covariate * 
lincom  1.soubinaryagency2nom + 1.soubinaryagency2nom#c.zloyalmedian, hr
*
*
*
estimates store model3
estout model3, cells(b(star fmt(3)) se(par fmt(3))) eform


*** COMPUTE Figure 2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {PP − NPP Difference} {{4 [M1−M4] × 1 Horizontal Point Estimates and 95% CIs}}. ****
** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]

lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3653231, eform(hr)
matrix model3zloyal = r(table)
mat list model3zloyal



**** COMPUTE Figure 3: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {PP − NPP Difference} {{4 [M1−M4] × 1 Horizontal Point Estimates and 95% CIs}.
** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]

** Generate 'manual' interaction variable ** 
generate loyalppdiff = soubinaryagency2nom*zloyalmedian

** Re-Estimate Model 3  with 'manual' interaction variable **
streg   zloyalmedian soubinaryagency2nom loyalppdiff  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr, distribution(weibull) hr vce(cluster sbagency)

estimate store model3a


margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9692858))

** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9692858))  contrast(atcontrast(r))
matrix model3azloyal = r(table)
mat list model3azloyal



estimates restore model3a

margins, predict(median time) at(loyalppdiff=(-0.6451644 1.711348))
margins, predict(median time) at(loyalppdiff=(-0.6451644 1.711348))  contrast(atcontrast(r))

matrix model3bzloyal = r(table)
mat list model3bzloyal





******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************



**** MODEL 4: WEIBULL MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****

streg   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr  i.sbagency reagan bush41 clinton bush43, distribution(weibull) hr vce(cluster sbagency)
*
estat ic
*
* Assess the Total Effect of Appointee Loyalty on Appointee Tenure in Priority Agencies: Priority Agency as Conditioning Covariate * 
lincom  zloyalmedian + 1.soubinaryagency2nom#c.zloyalmedian, hr
*
* Assess the Total Effect of Appointee Loyalty on Appointee Tenure in Priority Agencies: Appointee Loyalty as Conditioning Covariate * 
lincom  1.soubinaryagency2nom + 1.soubinaryagency2nom#c.zloyalmedian, hr
*
*
*
estimates store model4
estout model4, cells(b(star fmt(3)) se(par fmt(3))) eform



*** COMPUTE Figure 2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {PP − NPP Difference} {{4 [M1−M4] × 1 Horizontal Point Estimates and 95% CIs}}. ****
** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]

lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3653231, eform(hr)
matrix model4zloyal = r(table)
mat list model4zloyal




**** COMPUTE Figure 3: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {PP − NPP Difference} {{4 [M1−M4] × 1 Horizontal Point Estimates and 95% CIs}.
** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]

** Re-Estimate Model 4  with 'manual' interaction variable **
streg   zloyalmedian soubinaryagency2nom loyalppdiff  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr i.sbagency reagan bush41 clinton bush43, distribution(weibull) hr vce(cluster sbagency)

estimates store model4a

margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9692858))

** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9692858))  contrast(atcontrast(r))

matrix model4azloyal = r(table)
mat list model4azloyal



estimates restore model4a

margins, predict(median time) at(loyalppdiff=(-0.6451644 1.711348))
margins, predict(median time) at(loyalppdiff=(-0.6451644 1.711348))  contrast(atcontrast(r))

matrix model4bzloyal = r(table)
mat list model4bzloyal

**** COMPUTE Figure 4: Predicted Differential Survival Function based on Varying Levels of Appointee Loyalty [q = 10, q = 50, q = 90] Between Policy Priority versus Non−Policy Priority Executive Agencies: PP − NPP Difference {{[M2]: 3 Predicted Survival Curves}} *****

** NOTE: AL: Q=10: -0.6451644;  AL: Q=50: -0.1646416; & AL: Q=90: 1.711348 **

estimates restore model4a

stcurve, survival at1(zloyalmedian=1.711348) at2(zloyalmedian=-0.1646416) at3(zloyalmedian=-0.6451644) ///
ylabel(0(.1)1, angle(0) labsize(small)) xlabel(0(500)4000, labsize(small)) lcolor(black black black) ///
lpattern(solid dash dash_3dot) legend(lab(1 "Low Loyalty (q=10)") lab(2 "Moderate Loyalty (q=50)") lab(3 "High Loyalty (q=90)") rows(1))legend(size(vsmall)) ///
xline(365 730 1095 1460 1825 2190 2555 2920, lcolor(red%30) lpattern(solid)) ///
xtitle("Leadership Appointee Tenure Duration (Days)", size(small) margin(t=2)) ytitle("Survival Rate", size(small) margin(t=2)) ///
title("FIGURE 4", size(small)) subtitle("Predicted Survival Function Differential Between" "Policy Priority Agencies versus Non-Policy Priority Agencies" "[Varying Levels of Presidential Loyalty: Model 4]", size(small))

graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\Figure4.gph", replace




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*Figure 2

matrix pointmodel = model1zloyal[1,1], model2zloyal[1,1], model3zloyal[1,1], model4zloyal[1,1]

*
matrix cimodel = (model1zloyal[5,1], model2zloyal[5,1], model3zloyal[5,1], model4zloyal[5,1] \ model1zloyal[6,1], model2zloyal[6,1], model3zloyal[6,1], model4zloyal[6,1])

coefplot (matrix(pointmodel), ci((cimodel))), grid(none) xline(1, lcolor(red%40) lpattern(dash)) xtitle("Hazard Ratio", size(small) margin(t=2)) ylabel(1 "Model 1"  2 "Model 2"  3 "Model 3" 4 "Model 4", labsize(small) noticks) mlabel format(%9.3f) mlabposition(12) mlabsize(vsmall) xlabel(0(1)2, angle(0) labsize(small) format(%9.1f)) msymbol(o) mcolor(black) msize(small) title("FIGURE 2", size(small)) ciopts(lcolor(black)) legend(off) subtitle("Marginal Differential Effect of Presidential Loyalty on Appointee Tenure Hazard" "[Policy Priority Agencies versus Non-Policy Priority Agencies]", size(small))

graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\Figure2.gph", replace






***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************


*Figure 3

matrix pointmodel1 = model3azloyal[1,1], model3bzloyal[1,1], model4azloyal[1,1], model4bzloyal[1,1]

*
matrix cimodel1 = (model3azloyal[5,1], model3bzloyal[5,1], model4azloyal[5,1], model4bzloyal[5,1] \ model3azloyal[6,1], model3bzloyal[6,1], model4azloyal[6,1], model4bzloyal[6,1])

coefplot (matrix(pointmodel1), ci((cimodel1))), grid(none) xtitle("Predicted Number of Days", size(small) margin(t=2)) ylabel(1 `" "Model 3" "Interquartile Change" "' 2 `" "Model 3" "Interdecile Change" "' 3 `" "Model 4" "Interquartile Change" "' 4 `" "Model 4" "Interdecile Change" "', labsize(small) noticks) mlabel format(%9.0f) mlabposition(12) mlabsize(vsmall) xlabel(0(100)800, angle(0) labsize(small) format(%9.0f))   msymbol(o) mcolor(black) msize(small) title("FIGURE 3", size(small)) ciopts(lcolor(black)) legend(off) subtitle("Marginal Differential Effect of Presidential Loyalty on Median Appointee Tenure" "[Policy Priority Agencies versus Non-Policy Priority Agencies]", size(small))

graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\Figure3.gph", replace




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log close

